1# Copyright 2020 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore.context as context 20import mindspore.nn as nn 21from mindspore import Tensor 22from mindspore.ops import composite as C 23from mindspore.ops import operations as P 24 25context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 26 27 28class TanhNet(nn.Cell): 29 def __init__(self): 30 super(TanhNet, self).__init__() 31 self.tanh = P.Tanh() 32 33 def construct(self, x): 34 return self.tanh(x) 35 36 37class Grad(nn.Cell): 38 def __init__(self, network): 39 super(Grad, self).__init__() 40 self.grad = C.GradOperation(get_all=True, sens_param=True) 41 self.network = network 42 43 def construct(self, input_data, sens): 44 gout = self.grad(self.network)(input_data, sens) 45 return gout 46 47 48@pytest.mark.level0 49@pytest.mark.platform_x86_gpu_training 50@pytest.mark.env_onecard 51def test_Tanh(): 52 x_np = np.array( 53 [[0.28522366, 0.38033979, 1.54657853, -0.98530175, -0.54365635, 0.12652203, -1.33449938, -0.27737698], 54 [2.06282293, 0.84635078, 0.16628414, -0.91823183, -0.72023044, -0.09147043, -0.04166984, -1.5664763], 55 [-0.17157249, 0.44260951, -0.6683391, 1.13142613, 1.5536937, -0.32799768, -0.20016545, 0.06773927]], 56 dtype=np.float32) 57 dy_np = np.array( 58 [[0.44969849, -0.187879, -0.64300827, 1.36638774, 0.89930276, -0.23835229, -0.67771854, -1.88984999], 59 [2.00418801, 2.33336475, 0.00241747, 1.31558685, 0.06768817, -2.23008804, -0.26818366, -1.26873401], 60 [1.83694105, 0.5339005, 0.51117424, 0.49202378, -0.83297819, -0.71001219, 0.18913512, 0.65580389]], 61 dtype=np.float32) 62 63 x_ms = Tensor(x_np) 64 dy_ms = Tensor(dy_np) 65 66 net = TanhNet() 67 grad = Grad(net) 68 output = grad(x_ms, dy_ms) 69 70 expect = [[0.41501077, -0.16312202, -0.10675912, 0.58678646, 0.67828224, -0.23457714, -0.1643468, -1.75159405], 71 [0.12541081, 1.2251587, 0.00235184, 0.62396731, 0.04191568, -2.21153283, -0.26771853, -0.20311764], 72 [1.78391056, 0.44159236, 0.33690308, 0.16800483, -0.13651318, -0.63878956, 0.18175511, 0.65280384]] 73 74 assert np.allclose(output[0].asnumpy(), expect) 75 76@pytest.mark.level0 77@pytest.mark.platform_x86_gpu_training 78@pytest.mark.env_onecard 79def test_Tanh_fp16(): 80 np.random.seed(42) 81 x_np = np.random.randn(5, 3, 6).astype(np.float16) 82 dy_np = np.random.randn(5, 3, 6).astype(np.float16) 83 84 x_ms = Tensor(x_np) 85 dy_ms = Tensor(dy_np) 86 87 net = TanhNet() 88 grad = Grad(net) 89 output = grad(x_ms, dy_ms) 90 91 expect = [[[0.0766, 0.95, -0.474, -0.0568, -0.3713, -1.387], 92 [0.04626, 0.1521, 0.004135, -0.1771, -1.149, -0.341], 93 [-0.3235, -0.0666, -0.01921, 0.299, 0.7764, 0.1583]], 94 95 [[0.124, -0.0157, -0.3682, -0.0252, 0.05997, 0.51], 96 [-0.145, 0.2979, -0.01145, -1.019, 0.8125, 0.6914], 97 [0.562, -0.0848, 1.402, -0.5386, 0.318, 0.645]], 98 99 [[-0.9487, -0.04343, 0.02448, -0.4844, -0.939, 0.0666], 100 [-1.049, 0.433, -0.1724, 0.9604, -0.6377, -0.1241], 101 [0.7246, -0.1364, 0.2051, 1.132, -1.049, 0.1298]], 102 103 [[0.104, 0.3643, -0.6562, -1.202, 0.4688, 0.1294], 104 [0.2008, 0.3347, -0.2418, 0.07135, 0.1611, -0.1667], 105 [1.856, 0.1979, -1.048, 0.4443, -0.8574, 0.1329]], 106 107 [[1.156, -0.1322, 0.02069, 0.2241, 0.8164, 1.736], 108 [-0.2433, -0.05484, -0.848, -0.7197, -0.01453, 0.2637], 109 [0.1528, 0.6494, 0.006195, 1.307, -0.2024, 2.113]]] 110 111 assert np.allclose(output[0].asnumpy(), expect, rtol=1e-3, atol=1e-3) 112